I am trying to find some help with something that is called an "Adjusted Analysis" (or also Covariate Adjusted Logistic Regression); a typical response has been that I might just want multivariable logistic regression, but this is not quite what I am looking for. The trouble I have is with what exactly an "adjusted" analysis is.

As an example, I have at my disposal a software suite that performs this type of adjusted analysis. We have some genes and various clinical variables from patients; what the method seems to do is adjust the p-values of the genes. But I can't figure out why, or how. So I am trying to move outside of this software suite to truly understand what the underlying mathematics of this statistical technique is.

When I've posted this question in other places the response has been that I should just take more courses in statistics. So while acknowledging my short comings, I would like to please ask if anyone can point me in a somewhat correct direction. I have been trying to find resources to help however I think I am not posing my question correctly enough. As an aside I have a background in computer science and more recently I am branching into biostatistics and I don't like using black box software so I would eventually like to re-implement this technique in R.

Thank you for any help that can be offered. Please let me know if there is a way I can pose my question clearer.

• Could you explain what evidence do you have that the "adjusted regression" is not multiple regression? Because that's what it usually means. Commented May 19, 2011 at 18:39
• Sure, I think what it refers to is the following: The idea appears to be that in regression analysis the predictors and response variables are affected by a multiplicative factor (an observable covariate). Commonly suggested is the correction for body mass index, height and so on when measuring serum levels of certain compounds. I gathered that in typical medical data analysis, this adjustment is normally just done by dividing measured values by the confounding variables. More recently, adjustment methods are using varying coefficient regression to model this behviour more complexly. Commented May 19, 2011 at 18:52